Operation and maintenance management method and system for lactic acid bacteria fermentation equipment

Abstract

The invention relates to the field of equipment operation and maintenance management, and discloses an operation and maintenance management method and system for lactic acid bacteria fermentation equipment, and the method comprises the following steps: S1, monitoring the temperature, humidity, pressure and rotating speed parameters of the equipment in real time through a sensor and an Internet of Things technology, so as to obtain monitoring data; s2, preprocessing the monitoring data, and transmitting the preprocessed monitoring data to an operation and maintenance management platform for storage; s3, each equipment parameter of the monitoring data is compared with a preset fault threshold value, when any one equipment parameter reaches or exceeds the preset fault threshold value, it is judged that the current equipment operates abnormally, and otherwise, it is judged that the current equipment operates normally; s4, judging that the abnormally running equipment can automatically trigger an early warning mechanism to carry out early warning; and S5, carrying out secondary recheck on the equipment which triggers the early warning, obtaining a comprehensive operation evaluation index according to monitoring data processing, and judging whether the current equipment has a fault risk or not according to the comprehensive operation evaluation index.

Description

Technical Field

[0001] The present invention relates to the field of equipment operation and maintenance management, and in particular to a method and system for operation and maintenance management of lactic acid bacteria fermentation equipment. Background Art

[0002] Lactic acid bacteria are a type of bacteria that can use sugars for fermentation. They convert sugars into lactic acid under specific conditions (such as suitable temperature, humidity and pH value). In this process, lactic acid bacteria mainly metabolize through the glycolysis pathway (EMP pathway).

[0003] On the fermentation equipment, the process of lactic acid fermentation usually includes the following steps:

[0004] Raw material preparation: Select suitable sugar substances as fermentation substrates and carry out pretreatment.

[0005] Inoculation of lactic acid bacteria: Add cultured lactic acid bacteria to the raw materials in the fermentation equipment.

[0006] Control fermentation conditions: Adjust parameters such as temperature, humidity and pH value of the equipment to create an environment suitable for the growth and fermentation of lactic acid bacteria.

[0007] Fermentation process: Under the action of lactic acid bacteria, sugars are converted into lactic acid, and other flavor substances and active substances are produced at the same time.

[0008] Product processing: After fermentation is completed, the fermentation product is subjected to subsequent processing, such as cooling, crushing curd, mixing, acidification, homogenization, filling and refrigeration, etc., to obtain the final fermentation product.

[0009] During the entire fermentation process, the metabolic activity of lactic acid bacteria and the control parameters of the equipment are key factors that together determine the quality and taste of the fermented product. By precisely controlling these factors, lactic acid fermented products that meet the requirements can be produced.

[0010] The operation and maintenance of lactic acid bacteria fermentation equipment directly determines the quality of lactic acid bacteria. However, the existing operation and maintenance monitoring of lactic acid bacteria fermentation equipment still adopts the situation of independent detection of single equipment operating parameters, which leads to immediate alarm when there is a problem with the operating parameters of a single equipment, while ignoring the specific analysis. This will result in frequent alarms and even interference with normal equipment operation. Summary of the invention

[0011] The purpose of the present invention is to provide a method and system for operation and maintenance management of lactic acid bacteria fermentation equipment to solve the above technical problems.

[0012] The purpose of the present invention can be achieved through the following technical solutions:

[0013] A method for operation and maintenance management of lactic acid bacteria fermentation equipment, comprising:

[0014] S1. Use sensors and IoT technology to monitor the temperature, humidity, pressure and speed parameters of the equipment in real time to obtain monitoring data;

[0015] S2, pre-process the monitoring data and transmit it to the operation and maintenance management platform for storage;

[0016] S3. Compare each device parameter of the monitoring data with a preset fault threshold. When any device parameter reaches or exceeds the preset fault threshold, it is determined that the current device is operating abnormally; otherwise, it is determined that the current device is operating normally.

[0017] S4. The equipment that is judged to be operating abnormally will automatically trigger the early warning mechanism to issue an early warning;

[0018] S5. Conduct a second review of the equipment that triggers the early warning, obtain a comprehensive operation evaluation index based on the monitoring data, and determine whether the current equipment has a failure risk based on the comprehensive operation evaluation index.

[0019] As a further technical solution, the method for calculating and obtaining the comprehensive operation evaluation index is:

[0020] The monitoring data of the equipment is processed by the following formula:

[0021]

[0022] The comprehensive operation evaluation index F is calculated;

[0023] Among them, d mini is the lower limit of the ith parameter, d opti is the standard value of the ith parameter, d maxi The upper limit of the i-th parameter, d i is the measured value of the ith parameter, k1 and k2 are adjustment factors, and D i is the evaluation value of the i parameters, ω i is the weight coefficient of the i-th parameter, N is the total number of equipment operating parameters, XE i is the deviation coefficient of the i-th parameter in the last unit time.

[0024] As a further technical solution, the process of judging whether the current equipment has a risk of failure based on the comprehensive operation evaluation index is as follows:

[0025] Compare the comprehensive operation evaluation index F with the preset operation evaluation interval [F0, F1];

[0026] If F∈[F0, F1], it is judged that the equipment that triggers the warning does not have the risk of failure;

[0027] like It is judged that the equipment that triggers the early warning has a failure risk.

[0028] As a further technical solution, obtain the deviation coefficient XE of the i-th parameter in the previous unit time i The process is:

[0029] By formula:

[0030]

[0031] Calculate the deviation coefficient XE of the i-th parameter in the previous unit time i ;

[0032] Among them, t a ,t b is the starting point and end point of a unit time, d i (t) is the time-varying curve of the i-th parameter, d opti (t) Standard variation curve of the ith parameter over time, S i is the exponential coefficient of the ith parameter.

[0033] The exponential coefficient S i The calculation formula is:

[0034] S i =g i (d ih -d il )*δ i

[0035]

[0036] Among them, g i is the fluctuation coefficient of the ith parameter in the previous unit time, M is the total number of sampling times in the previous unit time, d ij is the observed value of the i-th parameter at the j-th sampling, d ih d il are the maximum and minimum values ​​of the i-th parameter in the previous unit time, δ i is the preset scale factor.

[0037] As a further technical solution, the method further includes:

[0038] S6. Based on the judgment result of whether the current equipment has a risk of failure, the equipment with a risk of failure is marked as a risk, and the equipment that triggers an early warning is marked as an early warning;

[0039] The equipment priority operation and maintenance sequence is comprehensively formulated based on the number of times each device is marked within a monitoring cycle and the frequent abnormal time periods obtained through analysis.

[0040] As a further technical solution, the steps of obtaining the frequent abnormal time period of each device are:

[0041] The time point of each risk or warning marking is used as the risk marking point, and the interval time ΔT between two adjacent risk marking points is obtained. If the interval time ΔT is less than the preset standard interval time ΔT0, the two adjacent risk marking points are regarded as an abnormal aggregation point;

[0042] If the current device has no abnormal gathering points, it is determined that the device has no frequent abnormal periods within a monitoring cycle;

[0043] If the current device has at least one abnormal aggregation point, it is determined that the device has a frequent abnormal period within a monitoring cycle, and the leftmost risk mark point and the rightmost risk mark point of each abnormal aggregation point on the time coordinate axis are taken as the frequent abnormal period;

[0044] By formula:

[0045]

[0046] Calculate the abnormal frequency coefficient R of the current device q ; is the total duration of the current device’s frequent abnormal period, T Z is the total duration of a monitoring cycle;

[0047] For devices with frequent abnormal periods, sort them in descending order according to the abnormal frequency coefficient of each device as the first device priority operation and maintenance sequence;

[0048] For devices without frequent abnormal periods, the devices are sorted in descending order according to the number of markings of each device in a monitoring cycle as the second device priority operation and maintenance sequence; and the first device priority operation and maintenance sequence is greater than the second device priority operation and maintenance sequence.

[0049] A lactic acid bacteria fermentation equipment operation and maintenance management system, comprising:

[0050] The equipment monitoring module uses sensors and IoT technology to monitor the temperature, humidity, pressure and speed parameters of the equipment in real time to obtain monitoring data;

[0051] Data transmission module, used to transmit the monitoring data to the operation and maintenance management platform for storage after pre-processing;

[0052] The early warning analysis module is used to compare each device parameter of the monitoring data with the preset fault threshold. When any device parameter reaches or exceeds the preset fault threshold, the current device is judged to be operating abnormally, otherwise it is judged to be operating normally;

[0053] The early warning trigger module automatically triggers the early warning mechanism when it determines that the equipment is operating abnormally.

[0054] The fault risk analysis module is used to conduct a secondary review of the equipment that triggers the early warning, obtain a comprehensive operation evaluation index based on the monitoring data, and determine whether the current equipment has a fault risk based on the comprehensive operation evaluation index;

[0055] The equipment operation and maintenance module, based on the judgment result of whether the current equipment has a risk of failure, will mark the equipment with a risk of failure and mark the equipment that triggers an early warning;

[0056] The equipment priority operation and maintenance sequence is comprehensively formulated based on the number of times each device is marked within a monitoring cycle and the frequent abnormal time periods obtained through analysis.

[0057] Beneficial effects of the present invention:

[0058] (1) The present invention can timely discover abnormal operation status of equipment by comparing preset fault thresholds with real-time monitoring data, and automatically trigger an early warning mechanism, thereby discovering potential faults in advance, avoiding production interruptions and product losses caused by equipment failures, and improving production stability and continuity;

[0059] (2) The present invention introduces secondary review and comprehensive operation evaluation index to conduct a more in-depth evaluation of the equipment that triggers the early warning; through a multi-level and comprehensive evaluation method, the failure risk of the equipment can be more accurately judged, providing a more reliable basis for operation and maintenance decisions; at the same time, this also provides strong support for the preventive maintenance and maintenance of the equipment, prolongs the service life of the equipment, and reduces the operation and maintenance costs; BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The present invention will be further described below in conjunction with the accompanying drawings.

[0061] Figure 1 It is a diagram of the method steps of the present invention;

[0062] Figure 2 It is a system structure block diagram of the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] See also Figure 1-Figure 2 As shown, the present invention is a method for operation and maintenance management of lactic acid bacteria fermentation equipment, comprising:

[0065] S1. Use sensors and IoT technology to monitor the temperature, humidity, pressure and speed parameters of the equipment in real time to obtain monitoring data;

[0066] S2, pre-process the monitoring data and transmit it to the operation and maintenance management platform for storage;

[0067] S3. Compare each device parameter of the monitoring data with a preset fault threshold. When any device parameter reaches or exceeds the preset fault threshold, it is determined that the current device is operating abnormally; otherwise, it is determined that the current device is operating normally.

[0068] S4. The equipment that is judged to be operating abnormally will automatically trigger the early warning mechanism to issue an early warning;

[0069] S5. Conduct a second review of the equipment that triggers the early warning, obtain a comprehensive operation evaluation index based on the monitoring data, and determine whether the current equipment has a failure risk based on the comprehensive operation evaluation index.

[0070] In this embodiment, through real-time monitoring by sensors and Internet of Things technology, key parameters such as temperature, humidity, pressure and rotation speed of the equipment can be accurately and quickly obtained, thereby ensuring the accuracy and timeliness of data for equipment operation and maintenance management; the real-time monitoring method, compared with traditional manual inspections, not only improves work efficiency, but also reduces errors caused by human factors; at the same time, it realizes the preprocessing and automatic storage of monitoring data, greatly simplifying the operation and maintenance management process; data preprocessing adopts the processing method in the existing technology, which can remove redundant and erroneous information and improve data availability, while automatic storage ensures the integrity and traceability of the data, providing support for subsequent equipment failure analysis and operation and maintenance decision-making. It provides strong support; subsequently, by comparing the preset fault threshold with the real-time monitoring data, it can timely detect the abnormal operating status of the equipment and automatically trigger the early warning mechanism, which can detect potential faults in advance and avoid production interruptions and product losses caused by equipment failures, thereby improving the stability and continuity of production; finally, it introduces secondary review and comprehensive operation evaluation index to conduct a more in-depth evaluation of the equipment that triggers the early warning; through a multi-level and comprehensive evaluation method, it can more accurately judge the failure risk of equipment and provide a more reliable basis for operation and maintenance decisions; at the same time, this also provides strong support for the preventive maintenance and maintenance of equipment, extends the service life of the equipment, and reduces the operation and maintenance costs.

[0071] The method for calculating the comprehensive operation evaluation index is:

[0072] The monitoring data of the equipment is processed by the following formula:

[0073]

[0074] The comprehensive operation evaluation index F is calculated;

[0075] Among them, d mini is the lower limit of the ith parameter, d opti is the standard value of the ith parameter, d maxi The upper limit of the i-th parameter, d i is the measured value of the ith parameter, k1 and k2 are adjustment factors, and D i is the evaluation value of the i parameters, ω i is the weight coefficient of the i-th parameter, N is the total number of equipment operating parameters, XE i is the deviation coefficient of the i-th parameter in the last unit time.

[0076] In this embodiment, by The actual operation status of each equipment operating parameter is evaluated. For positive parameters and negative parameters, the larger the positive parameter, the better, and the smaller the negative parameter, the better. Therefore, according to the category to which each equipment operating parameter belongs, the corresponding adjustment factor is assigned. For example, for temperature, the higher the temperature within the temperature range, the better the equipment runs. Therefore, k1 is assigned a value of 0.9 and k2 is assigned a value of 0.1. For negative parameters, the opposite is true. The value assigned to k1 is smaller than the value assigned to k2. The smaller the equipment operating parameter, the better.

[0077] After accurately evaluating each equipment operating parameter through the above method, The calculation is performed in a cumulative multiplication manner to obtain the overall operation evaluation of each device. Compared with the situation where an alarm is immediately issued when a problem occurs in the operation parameters of a single device without conducting a specific analysis, frequent alarms will result in interference with normal equipment operation. The present invention triggers an early warning through a single operation parameter and then finally confirms the equipment with a risk of failure through a secondary review, so as to carry out targeted treatment for subsequent timely operation and maintenance processing, thereby improving the actual operation quality of the equipment. At the same time, the equipment that frequently triggers early warnings can be marked to focus on it and realize efficient use of operation and maintenance forces.

[0078] The process of judging whether the current equipment has a risk of failure based on the comprehensive operation evaluation index is as follows:

[0079] Compare the comprehensive operation evaluation index F with the preset operation evaluation interval [F0, F1];

[0080] If F∈[F0, F1], it is judged that the equipment that triggers the warning does not have the risk of failure;

[0081] like It is judged that the equipment that triggers the early warning has a failure risk.

[0082] Through the above solution, timely and effective fault risk assessment can be performed on the equipment that triggers the early warning, providing support for subsequent equipment operation and maintenance management.

[0083] Get the deviation coefficient XE of the i-th parameter in the last unit time i The process is:

[0084] By formula:

[0085]

[0086] Calculate the deviation coefficient XE of the i-th parameter in the previous unit time i ;

[0087] Among them, t a ,t b is the starting point and end point of a unit time, d i (t) is the time-varying curve of the i-th parameter, d opti (t) Standard variation curve of the ith parameter over time, S i is the exponential coefficient of the ith parameter.

[0088] In this embodiment, by comparing the actual operating curve of each parameter in the historical unit time with the reference standard curve, it is obvious that |d i (t)-d opti The larger the (t)| is, the greater the deviation between the actual operating parameters and the standard operating parameters is, which means that the operating parameters are more unstable and therefore the probability of abnormality of the operating parameters is higher. The risk of each operating parameter is estimated by the deviation coefficient and dynamically updated to improve the accuracy of the comprehensive equipment operation evaluation index.

[0089] The exponential coefficient S i The calculation formula is:

[0090] S i =g i (d ih -d il )*δ i

[0091]

[0092] Among them, g i is the fluctuation coefficient of the ith parameter in the previous unit time, M is the total number of sampling times in the previous unit time, d ij is the observed value of the i-th parameter at the j-th sampling, d ih d il are the maximum and minimum values ​​of the i-th parameter in the previous unit time, δ iis the preset scale factor.

[0093] In this embodiment, the standard deviation and range S i =g i (d ih -d il )*δ i The data fluctuation of each operating parameter within a unit time is analyzed in a way. Obviously, the larger the standard deviation, the greater the fluctuation of the operating parameter. Combined with the extreme difference, the overall situation of the operating parameter can be accurately evaluated, and the prediction of subsequent failure risks can provide effective data support. At the same time, the influence on the deviation coefficient can also be dynamically adjusted. Obviously, the exponential coefficient S i The larger it is, the larger the coefficient of deviation is.

[0094] The method further comprises:

[0095] S6. Based on the judgment result of whether the current equipment has a risk of failure, the equipment with a risk of failure is marked as a risk, and the equipment that triggers an early warning is marked as an early warning;

[0096] The equipment priority operation and maintenance sequence is comprehensively formulated based on the number of times each device is marked within a monitoring cycle and the frequent abnormal time periods obtained through analysis.

[0097] As a further technical solution, the steps of obtaining the frequent abnormal time period of each device are:

[0098] The time point of each risk or warning marking is used as the risk marking point, and the interval time ΔT between two adjacent risk marking points is obtained. If the interval time ΔT is less than the preset standard interval time ΔT0, the two adjacent risk marking points are regarded as an abnormal aggregation point;

[0099] If the current device has no abnormal gathering points, it is determined that the device has no frequent abnormal periods within a monitoring cycle;

[0100] If the current device has at least one abnormal aggregation point, it is determined that the device has a frequent abnormal period within a monitoring cycle, and the leftmost risk mark point and the rightmost risk mark point of each abnormal aggregation point on the time coordinate axis are taken as the frequent abnormal period;

[0101] By formula:

[0102]

[0103] Calculate the abnormal frequency coefficient R of the current device q ; is the total duration of the current device’s frequent abnormal period, T Z is the total duration of a monitoring cycle;

[0104] For devices with frequent abnormal periods, sort them in descending order according to the abnormal frequency coefficient of each device as the first device priority operation and maintenance sequence;

[0105] For devices without frequent abnormal periods, the devices are sorted in descending order according to the number of markings of each device in a monitoring cycle as the second device priority operation and maintenance sequence; and the first device priority operation and maintenance sequence is greater than the second device priority operation and maintenance sequence.

[0106] In this embodiment, by marking the risk of failure for the equipment and marking the warning for the equipment that triggers the warning, the operation status of the equipment can be tracked more clearly, and the equipment priority operation and maintenance sequence can be formulated accordingly;

[0107] First, by calculating the interval time between adjacent risk marking points and identifying abnormal aggregation points, the frequent abnormal periods of the equipment can be accurately captured. This step not only helps to gain a deeper understanding of the failure mode of the equipment, but also provides key information for subsequent operation and maintenance decisions. Secondly, by introducing the abnormal frequency coefficient as an evaluation indicator, the failure risk level of the equipment can be quantified. By comparing the abnormal frequency coefficients of different equipment, it is possible to objectively and impartially determine which equipment requires priority operation and maintenance. Finally, based on whether the equipment has frequent abnormal periods, the first equipment priority operation and maintenance sequence and the second equipment priority operation and maintenance sequence are respectively formulated. This takes into account both the failure risk level of the equipment and the overall operation and maintenance needs of the equipment, ensuring the rational allocation and efficient use of operation and maintenance resources.

[0108] A lactic acid bacteria fermentation equipment operation and maintenance management system, comprising:

[0109] The equipment monitoring module uses sensors and IoT technology to monitor the temperature, humidity, pressure and speed parameters of the equipment in real time to obtain monitoring data;

[0110] Data transmission module, used to transmit the monitoring data to the operation and maintenance management platform for storage after pre-processing;

[0111] The early warning analysis module is used to compare each device parameter of the monitoring data with the preset fault threshold. When any device parameter reaches or exceeds the preset fault threshold, the current device is judged to be operating abnormally, otherwise it is judged to be operating normally;

[0112] The early warning trigger module automatically triggers the early warning mechanism when it determines that the equipment is operating abnormally.

[0113] The fault risk analysis module is used to conduct a secondary review of the equipment that triggers the early warning, obtain a comprehensive operation evaluation index based on the monitoring data, and determine whether the current equipment has a fault risk based on the comprehensive operation evaluation index;

[0114] The equipment operation and maintenance module, based on the judgment result of whether the current equipment has a risk of failure, will mark the equipment with a risk of failure and mark the equipment that triggers an early warning;

[0115] The equipment priority operation and maintenance sequence is comprehensively formulated based on the number of times each device is marked within a monitoring cycle and the frequent abnormal time periods obtained through analysis.

[0116] It should be noted that the calculation formulas and various parameters involved in the calculations in the present invention have been dimensionally processed in advance, and the process of dimensionless processing is well known in the industry and will not be described here.

[0117] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for operation and maintenance management of lactic acid bacteria fermentation equipment, characterized in that: include: S1. Use sensors and IoT technology to monitor the temperature, humidity, pressure and speed parameters of the equipment in real time to obtain monitoring data; S2, pre-process the monitoring data and transmit it to the operation and maintenance management platform for storage; S3. Compare each device parameter of the monitoring data with a preset fault threshold. When any device parameter reaches or exceeds the preset fault threshold, it is determined that the current device is operating abnormally; otherwise, it is determined that the current device is operating normally. S4. The equipment that is judged to be operating abnormally will automatically trigger the early warning mechanism to issue an early warning; S5. Conduct a second review of the equipment that triggers the early warning, obtain a comprehensive operation evaluation index based on the monitoring data, and determine whether the current equipment has a failure risk based on the comprehensive operation evaluation index.

2. The operation and maintenance management method of lactic acid bacteria fermentation equipment according to claim 1, characterized in that: The method for calculating the comprehensive operation evaluation index is: The monitoring data of the equipment is processed by the following formula: The comprehensive operation evaluation index F is calculated; Among them, d mini is the lower limit of the ith parameter, d opti is the standard value of the ith parameter, d maxi The upper limit of the i-th parameter, d i is the measured value of the ith parameter, k1 and k2 are adjustment factors, and D i is the evaluation value of the i parameters, ω i is the weight coefficient of the i-th parameter, N is the total number of equipment operating parameters, XE i is the deviation coefficient of the i-th parameter in the last unit time.

3. The operation and maintenance management method of lactic acid bacteria fermentation equipment according to claim 2, characterized in that: The process of judging whether the current equipment has a risk of failure based on the comprehensive operation evaluation index is as follows: Compare the comprehensive operation evaluation index F with the preset operation evaluation interval [F0, F1]; If F∈[F0, F1], it is judged that the equipment that triggers the warning does not have the risk of failure; like It is judged that the equipment that triggers the early warning has a failure risk.

4. The operation and maintenance management method of lactic acid bacteria fermentation equipment according to claim 3, characterized in that: Get the deviation coefficient XE of the i-th parameter in the last unit time i The process is: By formula: Calculate the deviation coefficient XE of the i-th parameter in the previous unit time i ; Among them, t a ,t b is the starting point and end point of a unit time, d i (t) is the time-varying curve of the i-th parameter, d opti (t) Standard variation curve of the ith parameter over time, S i is the exponential coefficient of the ith parameter.

5. The operation and maintenance management method of lactic acid bacteria fermentation equipment according to claim 4, characterized in that: The exponential coefficient S i The calculation formula is: S i =g i (d ih -d il )*d i Among them, g i is the fluctuation coefficient of the ith parameter in the previous unit time, M is the total number of sampling times in the previous unit time, d ij is the observed value of the i-th parameter at the j-th sampling, d ih ,d il are the maximum and minimum values ​​of the i-th parameter in the previous unit time, δ i is the preset scale factor.

6. The operation and maintenance management method of lactic acid bacteria fermentation equipment according to claim 1 or 5, characterized in that: The method further comprises: S6. Based on the judgment result of whether the current equipment has a risk of failure, the equipment with a risk of failure is marked as a risk, and the equipment that triggers an early warning is marked as an early warning; The equipment priority operation and maintenance sequence is comprehensively formulated based on the number of times each device is marked within a monitoring cycle and the frequent abnormal time periods obtained through analysis.

7. The operation and maintenance management method of lactic acid bacteria fermentation equipment according to claim 6, characterized in that: The steps to obtain the frequent abnormal time period of each device are: The time point of each risk or warning marking is used as the risk marking point, and the interval time ΔT between two adjacent risk marking points is obtained. If the interval time ΔT is less than the preset standard interval time ΔT0, the two adjacent risk marking points are regarded as an abnormal aggregation point; If the current device has no abnormal gathering points, it is determined that the device has no frequent abnormal periods within a monitoring cycle; If the current device has at least one abnormal aggregation point, it is determined that the device has a frequent abnormal period within a monitoring cycle, and the leftmost risk mark point and the rightmost risk mark point of each abnormal aggregation point on the time coordinate axis are taken as the frequent abnormal period; By formula: Calculate the abnormal frequency coefficient R of the current device q ; is the total duration of the current device’s frequent abnormal period, T Z is the total duration of a monitoring cycle; For devices with frequent abnormal periods, sort them in descending order according to the abnormal frequency coefficient of each device as the first device priority operation and maintenance sequence; For devices without frequent abnormal periods, the devices are sorted in descending order according to the number of markings of each device in a monitoring cycle as the second device priority operation and maintenance sequence; and the first device priority operation and maintenance sequence is greater than the second device priority operation and maintenance sequence.

8. A lactic acid bacteria fermentation equipment operation and maintenance management system, the system is used to implement the lactic acid bacteria fermentation equipment operation and maintenance management method according to claim 6, characterized in that: include: The equipment monitoring module uses sensors and IoT technology to monitor the temperature, humidity, pressure and speed parameters of the equipment in real time to obtain monitoring data; Data transmission module, used to transmit the monitoring data to the operation and maintenance management platform for storage after pre-processing; The early warning analysis module is used to compare each equipment parameter of the monitoring data with the preset fault threshold. When any equipment parameter reaches or exceeds the preset fault threshold, the current equipment is judged to be operating abnormally, otherwise the current equipment is judged to be operating normally; The early warning trigger module automatically triggers the early warning mechanism when it determines that the equipment is operating abnormally. The fault risk analysis module is used to conduct a secondary review of the equipment that triggers the early warning, obtain a comprehensive operation evaluation index based on the monitoring data, and determine whether the current equipment has a fault risk based on the comprehensive operation evaluation index; The equipment operation and maintenance module, based on the judgment result of whether the current equipment has a risk of failure, will mark the equipment with a risk of failure and mark the equipment that triggers an early warning; The equipment priority operation and maintenance sequence is comprehensively formulated based on the number of times each device is marked within a monitoring cycle and the frequent abnormal time periods obtained through analysis.

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